
Data Science Experts in Stuttgart
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Meet FRATCH Experts in Stuttgart, who have recently used Data Science
Karin A.
Last position:
AI Benchmark Engineer | Native language specialist German at Lilt
- Task Engineering: Evaluating Coding Agents.
- Asset Creation: Building realistic task environments using datasets and files in German. Crucially, these assets must remain in the target language to genuinely measure multilingual handling.
- Prompting & Translation: finding failure points where AI does not work, in German.
- Implementation & Verification: Supporting the development of robust solutions (reference implementations) and write highly reliable, deterministic verifier scripts (using rubric-based judging only when strictly necessary).
- Calibration & Execution: Analyze execution logs and calibrate task difficulty (Easy to Very Hard) using standard Terminal-Bench run configurations against various model tiers (Haiku, Opus).
- Quality Assurance: Participation in a rigorous, 4-layer human quality control process (creation, human review, calibration review, and audit) alongside automated LLM-based checks to ensure fairness, grammatical accuracy, and benchmark integrity.
- Linguistic Review: Reviewing AI benchmark tasks across Hindi, Arabic, Japanese, Chinese, Czech and Turkish.
Mustafa Ö.
Last position:
Data & Business Analyst at Self-Employed / Freelancer
External consultant for various projects in the automotive industry.
Data-driven business and performance analysis
Analysis, interpretation and validation of complex business data
Identification of trends, risks and potential areas for improvement
Translation of analytical findings into actionable business recommendations
Preparation and presentation of results for business stakeholders
Friederike B.
Last position:
Independent Consultant and Trainer at Friederike Bohm Consulting
- Consulting on lean, logistics, AI applications and process optimization
- Customized concepts for digitalization, change & transformation
- Adaptive training and workshops for professionals and managers
Chaima D.
Last position:
Data Scientist Intern at Marelli Automotive Lighting
- Developed and deployed a deep learning model for automated keypoint detection in headlamp light distributions.
- Prepared and processed datasets, and selected VGG16 after benchmarking CNN architectures for the best accuracy efficiency trade-off.
- Delivered a Flask REST API, containerized with Docker, and integrated the solution into an existing internal system, enabling automated and efficient evaluation of headlamp designs.
Dean R.
Last position:
CEO / Chief Scientist at ENUM
- Blockchain platform technology
- Blockchain digital platform / Digital Economy.
Akshata N.
Last position:
Data Science Intern at Unified Mentor
- Improved predictive model accuracy by 18% using advanced feature engineering.
- Automated data pipelines via Python ETL, reducing manual work by 25%.
- Documented data flows to identify automation potential and support digitalization projects.
Divij W.
Last position:
Data Scientist at Daimler R&D, Daimler AG
- Mercedes Me is an app that connects your phone to several features in the car
- Implemented analytical KPIs for the Digital Drivers Log (Fahrtenbuch) feature
- Used PySpark on Databricks
Christoph D.
Last position:
Agentic RAG AI System at Financial Services Provider
- Developed an agentic RAG system to support the development organization.
- Technologies: Python, LangGraph, Qdrant, Claude Code, GitHub.
Augusto M.
Last position:
Managing Director/Co-Founder at Merx GmbH
- Responsible for data analytics and marketing/sales.
- This is a part-time job (1-2 days a week).
Discover over 15,000 top freelancers
Statistics of experts using Data Science
Aggregated from the professional profiles of matched freelancers.
Experience
16 years (Germany: 14 years)

Position duration
2.8 years (Germany: 2.2 years)

Positions per freelancer
8 (Germany: 9)

Top business areas
Business Intelligence, Information Technology, Product Development

Top industries
Automotive, Information Technology, Professional Services

Certification focus areas
Business Intelligence, Information Technology, Product Development
Bachelor's degree or higher
100% (Germany: 97%)
Master's degree or higher
88% (Germany: 80%)
Doctorate
13% (Germany: 22%)

Certifications per freelancer
2 (Germany: 3)

Most common languages
German, English, French

Speak two or more languages
100% (Germany: 97%)
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Stuttgart are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Stuttgart using Data Science
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Data Science experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Automotive (78%)
- Information Technology (67%)
- Professional Services (56%)
- Education (44%)
- Banking and Finance (44%)
- Transportation (44%)
- Manufacturing (44%)
- Healthcare (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Data Science delivers
Data Science combines statistics, programming, domain knowledge and machine learning to extract useful signals from structured and unstructured data. Companies use it to forecast demand, detect anomalies, personalise services, optimise operations and support better business decisions. Strong work connects analytical results to measurable product or process outcomes.
Typical project work
Data Science specialists contribute across the full path from raw data to operational use:
- Define business questions, target variables and success criteria
- Clean, join and validate data from business and digital systems
- Explore patterns with statistical analysis and visualisation
- Train, test and explain predictive or classification models
- Deliver dashboards, scoring services or decision-ready reports
Ecosystem and tooling
Python is common for analysis and modelling, with pandas, NumPy, scikit-learn, SciPy and notebook environments forming a practical core. Specialists may also use PyTorch or TensorFlow for deep learning, SQL and dbt for data transformation, and tools such as MLflow for experiment tracking. Cloud services, Docker, APIs and orchestration connect models with production systems.
When companies need specialists
Freelance expertise helps when internal teams have valuable data but lack the capacity to turn it into a dependable solution. It is useful for a first feasibility study, a model rebuild, a migration to a modern data stack or the handover of a prototype into production. In Stuttgart, projects may span automotive, manufacturing, mobility, finance, healthcare and research, with remote or on-site collaboration depending on data access and team routines.
What strong professionals bring
Good Data Science professionals clarify assumptions before selecting an algorithm. They understand sampling bias, leakage, uncertainty, drift and data quality, and they explain trade-offs to both technical and business stakeholders. Beyond model accuracy, they consider maintainability, reproducibility, privacy, monitoring and how people will act on the result.
Evaluating the engagement
A useful brief names the decision the analysis should improve, the available data sources and the constraints around security, latency and interpretability. During review, ask for evidence of comparable analytical work, clear validation methods and a plan for deployment or handover. The best engagement leaves behind documented pipelines, readable code, tested outputs and guidance that the internal team can maintain.
Frequently asked questions
Key details about Data Science, drawn from the questions we get asked most.
Companies use Data Science to find patterns in data and turn them into forecasts, recommendations, risk signals and operational decisions. Typical work includes demand forecasting, customer segmentation, fraud detection, quality analysis and predictive maintenance.
Data Science often focuses on prediction, experimentation and automated decisions, while business intelligence usually explains what has already happened through reports and dashboards. Data analytics can cover both areas, so the boundaries depend on the project and the skills required.
A capable Data Science specialist should understand SQL, data modelling, statistics, software development and data visualisation. Experience with cloud infrastructure, APIs, Docker, experiment tracking and model monitoring is valuable when analysis must run reliably in a product or business process.
The right level for Data Science depends on the risk, data quality and delivery stage rather than on a fixed tenure requirement. A small exploratory analysis may need a focused specialist, while a production model benefits from experience with deployment, validation, monitoring and stakeholder adoption.
Much Data Science work can be completed remotely when secure access to datasets, documentation and computing environments is available. On-site sessions in Stuttgart can help with sensitive data, workshops and close collaboration with product, manufacturing or research teams.
Before starting Data Science work, define access controls, data ownership, retention rules and the permitted use of personal or confidential information. A clear environment for development and a documented approval process reduce security risks without blocking useful analysis.
Quality Data Science work has a clear problem definition, a defensible baseline and validation that reflects real usage. Look for transparent assumptions, reproducible data preparation, meaningful error analysis and documentation explaining when the model should or should not be trusted.
A Data Science engagement may deliver cleaned datasets, analysis notebooks, feature pipelines, trained models, evaluation reports, dashboards or an integrated scoring service. The handover should also cover setup instructions, limitations, monitoring needs and recommendations for future improvement.
The average hourly rate of freelancers in Stuttgart, Germany who have used Data Science in their recent projects is 100 €, which corresponds to a daily rate of about 802 € based on an 8-hour working day.
Of the freelancers in Stuttgart, Germany who have used Data Science in their recent projects, 100% hold at least a Bachelor's degree, 88% hold at least a Master's degree, and 13% hold a doctorate.
On average, freelancers in Stuttgart, Germany who have used Data Science in their recent projects have 16 years of professional experience, with a single engagement typically lasting around 2.8 years.
The most common languages among freelancers in Stuttgart, Germany who have used Data Science in their recent projects are German (100%), English (100%), and French (22%).
The most common industries among freelancers in Stuttgart, Germany who have used Data Science in their recent projects are Automotive (78%), Information Technology (67%), and Professional Services (56%).
The most common business areas among freelancers in Stuttgart, Germany who have used Data Science in their recent projects are Business Intelligence (89%), Information Technology (78%), and Product Development (78%).
Main locations of FRATCH Experts, who have recently used Data Science
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
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